Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
The cyclotron radiation emission spectroscopy (CRES) technique pioneered by Project 8 measures electromagnetic radiation from individual electrons gyrating in a background magnetic field to construct a highly precise energy spectrum for beta decay studies and other applications. The detector, magnet...
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| Veröffentlicht in: | New journal of physics 2020-03, Vol.22 (3), p.33004 |
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| creator | Esfahani, A Ashtari Böser, S Buzinsky, N Cervantes, R Claessens, C Viveiros, L de Fertl, M Formaggio, J A Gladstone, L Guigue, M Heeger, K M Johnston, J Jones, A M Kazkaz, K LaRoque, B H Lindman, A Machado, E Monreal, B Morrison, E C Nikkel, J A Novitski, E Oblath, N S Pettus, W Robertson, R G H Rybka, G Saldaña, L Sibille, V Schram, M Slocum, P L Sun, Y-H Thümmler, T VanDevender, B A Weiss, T E Wendler, T Zayas, E |
| description | The cyclotron radiation emission spectroscopy (CRES) technique pioneered by Project 8 measures electromagnetic radiation from individual electrons gyrating in a background magnetic field to construct a highly precise energy spectrum for beta decay studies and other applications. The detector, magnetic trap geometry and electron dynamics give rise to a multitude of complex electron signal structures which carry information about distinguishing physical traits. With machine learning models, we develop a scheme based on these traits to analyze and classify CRES signals. Proper understanding and use of these traits will be instrumental to improve cyclotron frequency reconstruction and boost the potential of Project 8 to achieve world-leading sensitivity on the tritium endpoint measurement in the future. |
| doi_str_mv | 10.1088/1367-2630/ab71bd |
| format | Article |
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The detector, magnetic trap geometry and electron dynamics give rise to a multitude of complex electron signal structures which carry information about distinguishing physical traits. With machine learning models, we develop a scheme based on these traits to analyze and classify CRES signals. Proper understanding and use of these traits will be instrumental to improve cyclotron frequency reconstruction and boost the potential of Project 8 to achieve world-leading sensitivity on the tritium endpoint measurement in the future.</description><identifier>ISSN: 1367-2630</identifier><identifier>EISSN: 1367-2630</identifier><identifier>DOI: 10.1088/1367-2630/ab71bd</identifier><identifier>CODEN: NJOPFM</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Beta decay ; Cyclotron frequency ; Cyclotron radiation ; Cyclotrons ; Electromagnetic radiation ; Emission spectroscopy ; Energy spectra ; Machine learning ; neutrino mass ; NUCLEAR PHYSICS AND RADIATION PHYSICS ; Physics ; Signal classification ; Spectrum analysis ; support vector machine ; Tritium</subject><ispartof>New journal of physics, 2020-03, Vol.22 (3), p.33004</ispartof><rights>2020 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft</rights><rights>2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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Proper understanding and use of these traits will be instrumental to improve cyclotron frequency reconstruction and boost the potential of Project 8 to achieve world-leading sensitivity on the tritium endpoint measurement in the future.</description><subject>Beta decay</subject><subject>Cyclotron frequency</subject><subject>Cyclotron radiation</subject><subject>Cyclotrons</subject><subject>Electromagnetic radiation</subject><subject>Emission spectroscopy</subject><subject>Energy spectra</subject><subject>Machine learning</subject><subject>neutrino mass</subject><subject>NUCLEAR PHYSICS AND RADIATION PHYSICS</subject><subject>Physics</subject><subject>Signal classification</subject><subject>Spectrum analysis</subject><subject>support vector 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machine learning in project 8</title><author>Esfahani, A Ashtari ; Böser, S ; Buzinsky, N ; Cervantes, R ; Claessens, C ; Viveiros, L de ; Fertl, M ; Formaggio, J A ; Gladstone, L ; Guigue, M ; Heeger, K M ; Johnston, J ; Jones, A M ; Kazkaz, K ; LaRoque, B H ; Lindman, A ; Machado, E ; Monreal, B ; Morrison, E C ; Nikkel, J A ; Novitski, E ; Oblath, N S ; Pettus, W ; Robertson, R G H ; Rybka, G ; Saldaña, L ; Sibille, V ; Schram, M ; Slocum, P L ; Sun, Y-H ; Thümmler, T ; VanDevender, B A ; Weiss, T E ; Wendler, T ; Zayas, E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c475t-f696333d7cf4f35e4afb06e559c55d9a140e787e32d7970a61ed1e883ba87663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Beta decay</topic><topic>Cyclotron frequency</topic><topic>Cyclotron radiation</topic><topic>Cyclotrons</topic><topic>Electromagnetic radiation</topic><topic>Emission 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| subjects | Beta decay Cyclotron frequency Cyclotron radiation Cyclotrons Electromagnetic radiation Emission spectroscopy Energy spectra Machine learning neutrino mass NUCLEAR PHYSICS AND RADIATION PHYSICS Physics Signal classification Spectrum analysis support vector machine Tritium |
| title | Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8 |
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